P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016)

Uri Simonsohn, Leif D Nelson and Joseph P Simmons

PLOS ONE, 2019, vol. 14, issue 3, 1-5

Abstract: p-curve, the distribution of significant p-values, can be analyzed to assess if the findings have evidential value, whether p-hacking and file-drawering can be ruled out as the sole explanations for them. Bruns and Ioannidis (2016) have proposed p-curve cannot examine evidential value with observational data. Their discussion confuses false-positive findings with confounded ones, failing to distinguish correlation from causation. We demonstrate this important distinction by showing that a confounded but real, hence replicable association, gun ownership and number of sexual partners, leads to a right-skewed p-curve, while a false-positive one, respondent ID number and trust in the supreme court, leads to a flat p-curve. P-curve can distinguish between replicable and non-replicable findings. The observational nature of the data is not consequential.

Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0213454

DOI: 10.1371/journal.pone.0213454

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